SoroushVahidi/scidocs huggingface dataset:
Time Series Forecasting Based on Augmented Long Short-Term Memory
(2017)

In this paper, we use recurrent autoencoder model to predict the time series in single and multiple steps ahead. Previous prediction methods, such as recurrent neural network (RNN) and deep belief network (DBN) models, cannot learn long term dependencies. And conventional long short-term memory (LSTM) model doesn’t remember recent inputs. Combining LSTM and autoencoder (AE), the proposed model can capture long-term dependencies across data points and uses features extracted from recent observations for augmenting LSTM at the same time. Based on comprehensive experiments, we show that the proposed methods significantly improves the state-of-art performance on chaotic time series benchmark and also has better performance on real-world data. Both single-output and multiple-output predictions are investigated.
Nasal base reduction by alar release: a laboratory evaluation.
(2009)

BACKGROUND
When reducing the broad nasal base, there is a limit to the amount of soft tissue that can be resected, beyond which the anatomy distorts and the nostrils become stenotic (if resection enters the nostril). Alar mobilization by freeing soft-tissue attachments helps. This study purported to examine the nature of those attachments and the extent of medialization.


METHODS
The supporting tissues of the ala were sequentially divided in 16 fresh hemifacial cadavers. Key structures included the following: (1) the soft tissues and pyriform ligament of the anterior maxilla, (2) the periosteum posterior to the pyriform rim (in the bony nasal vault), and (3) the soft tissues along the horizontal pyriform rim. After release of each tethering region, the ala-pyriform distance was measured.


RESULTS
After releasing the anterior maxillary periosteum and pyriform ligament along the vertical pyriform rim, the ala-pyriform distance was reduced by a mean of 1.9 mm. After releasing the periosteum posterior to the pyriform rim (in the nasal vault), it was reduced by a mean of 1.7 mm. Releasing the soft tissues (which were thick medially) of the horizontal pyriform rim reduced the mean distance 1.0 mm for a total of 4.6 mm. Medialization resulting from anterior and posterior releases was significantly greater than that from the horizontal pyriform rim (p < 0.0006 and p < 0.015, respectively), but they were not significantly different from one another.


CONCLUSIONS
This cadaver study confirmed the role of the stabilizing effect of the pyriform ligament and the periosteum lateral and posterior to the pyriform rim. The total release was substantial, suggesting a clinical means of achieving tension-free alar medialization.
Deep Network Guided Proof Search
(2017)

Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go. Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification. Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved. Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.
Procedural facade variations from a single layout
(2013)

We introduce a framework to generate many variations of a facade design that look similar to a given facade layout. Starting from an input image, the facade is hierarchically segmented and labeled with a collection of manual and automatic tools. The user can then model constraints that should be maintained in any variation of the input facade design. Subsequently, facade variations are generated for different facade sizes, where multiple variations can be produced for a certain size. Computing such new facade variations has many unique challenges, and we propose a new algorithm based on interleaving heuristic search and quadratic programming. In contrast to most previous work, we focus on the generation of new design variations and not on the automatic analysis of the input's structure. Adding a modeling step with the user in the loop ensures that our results routinely are of high quality.
Survey of Insurance Fraud Detection Using Data Mining Techniques
(2013)

62 Abstract— With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.
A Region Ensemble for 3-D Face Recognition
(2008)

In this paper, we introduce a new system for 3D face recognition based on the fusion of results from a committee of regions that have been independently matched. Experimental results demonstrate that using 28 small regions on the face allow for the highest level of 3D face recognition. Score-based fusion is performed on the individual region match scores and experimental results show that the Borda count and consensus voting methods yield higher performance than the standard sum, product, and min fusion rules. In addition, results are reported that demonstrate the robustness of our algorithm by simulating large holes and artifacts in images. To our knowledge, no other work has been published that uses a large number of 3D face regions for high-performance face matching. Rank one recognition rates of 97.2% and verification rates of 93.2% at a 0.1% false accept rate are reported and compared to other methods published on the face recognition grand challenge v2 data set.
Deformation Modeling for Robust 3D Face Matching
(2006)

Face recognition based on 3D surface matching is promising for overcoming some of the limitations of current 2D image-based face recognition systems. The 3D shape is generally invariant to the pose and lighting changes, but not invariant to the nonrigid facial movement such as expressions. Collecting and storing multiple templates to account for various expressions for each subject in a large database is not practical. We propose a facial surface modeling and matching scheme to match 2.5D facial scans in the presence of both nonrigid deformations and pose changes (multiview) to a stored 3D face model with neutral expression. A hierarchical geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A user-specific (3D) deformable model is built for each subject in the gallery with respect to the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and pose changes simultaneously. A fully automatic and prototypic deformable model based 3D face matching system has been developed. Experimental results demonstrate that the proposed deformation modeling scheme increases the 3D face matching accuracy in comparison to matching with 3D neutral models by 7 and 10 percentage points, respectively, on a subset of the FRGC v2.0 3D benchmark and the MSU multiview 3D face database with expression variations.
Monitoring critical temperatures in permanent magnet synchronous motors using low-order thermal models
(2014)

Monitoring critical temperatures in electric motors is crucial for preventing shortened motor life spans due to excessive thermal stress. With regard to interior permanent magnet synchronous motors (IPMSM), critical temperatures typically occur in the magnets and in the stator end winding. As directly measuring temperatures, especially on the rotating part, is costly, sensitive and thus not applicable with respect to automotive applications, model-based approaches are preferred. In this paper, two low-order thermal models for accurate temperature estimations in the permanent magnets, the winding and the end winding are introduced and compared. The model parameters are estimated solely based on experimental data via a multistep identification approach for linear parameter-varying systems. The model performances are validated by extensive experimental results based on a high-speed PMSM typically used as traction motor in subcompact electric cars.
RC-RCD clamp circuit for ringing losses reduction in a flyback converter
(2006)

An RCD clamp circuit is usually used in flyback converters, in order to limit the voltage spikes caused by leakage transformer inductance. Oscillation ringing appears due to the clamp diode, which deteriorates the converter's power rate. This brief describes this ringing phenomenon and the use of an RC-RCD clamp circuit for damping the clamp diode's oscillation. This clamp circuit is capable for improving a flyback converter's power ratio.
A secure and unclonable embedded system using instruction-level PUF authentication
(2014)

In this paper we present a secure and unclonable embedded system design that can target either an FPGA or an ASIC technology. The premise of the security is that the executed machine code and the executing environment (the embedded processor) will authenticate each other at a per-instruction basis using Physical Unclonable Functions (PUFs) that are built into the processor. The PUFs ensure that the execution of the binary code may only proceed if the binary is compiled with the correct intrinsic knowledge of the PUFs, and that such intrinsic knowledge is virtually unique to each processor and therefore unclonable. We will explain how to implement and integrate the PUFs into the processor's execution environment such that each instruction is authenticated and de-obfuscated on-demand and how to transform an ordinary binary executable into PUF-aware, obfuscated binaries. We will also present a prototype system on a Xilinx Spartan6-based FPGA board.
Augmenting feature model through customer preference mining by hybrid sentiment analysis
(2017)

A feature model is an essential tool to identify variability and commonality within a product line of an enterprise, assisting stakeholders to configure product lines and to discover opportunities for reuse. However, the number of product variants needed to satisfy individual customer needs is still an open question, as feature models do not incorporate any direct customer preference information. In this paper, we propose to incorporate customer preference information into feature models using sentiment analysis of user-generated online product reviews. The proposed sentiment analysis method is a hybrid combination of affective lexicons and a rough-set technique. It is able to predict sentence sentiments for individual product features with acceptable accuracy, and thus augment a feature model by integrating positive and negative opinions of the customers. Such opinionated customer preference information is regarded as one attribute of the features, which helps to decide the number of variants needed within a product line. Finally, we demonstrate the feasibility and potential of the proposed method via an application case of Kindle Fire HD tablets.
Inferring tumor progression from genomic heterogeneity.
(2010)

Cancer progression in humans is difficult to infer because we do not routinely sample patients at multiple stages of their disease. However, heterogeneous breast tumors provide a unique opportunity to study human tumor progression because they still contain evidence of early and intermediate subpopulations in the form of the phylogenetic relationships. We have developed a method we call Sector-Ploidy-Profiling (SPP) to study the clonal composition of breast tumors. SPP involves macro-dissecting tumors, flow-sorting genomic subpopulations by DNA content, and profiling genomes using comparative genomic hybridization (CGH). Breast carcinomas display two classes of genomic structural variation: (1) monogenomic and (2) polygenomic. Monogenomic tumors appear to contain a single major clonal subpopulation with a highly stable chromosome structure. Polygenomic tumors contain multiple clonal tumor subpopulations, which may occupy the same sectors, or separate anatomic locations. In polygenomic tumors, we show that heterogeneity can be ascribed to a few clonal subpopulations, rather than a series of gradual intermediates. By comparing multiple subpopulations from different anatomic locations, we have inferred pathways of cancer progression and the organization of tumor growth.
Will NoSQL Databases Live Up to Their Promise?
(2010)

Many organizations collect vast amounts of customer, scientific, sales, and other data for future analysis. Traditionally, most of these organizations have stored structured data in relational databases for subsequent access and analysis. However, a growing number of developers and users have begun turning to various types of nonrelational, now frequently called NoSQL-databases. Nonrelational databases, including hierarchical, graph, and object-oriented databases-have been around since the late 1960s. However, new types of NoSQL databases are being developed. And only now are they beginning to gain market traction. Different NoSQL databases take different approaches. What they have in common is that they're not relational. Their primary advantage is that, unlike relational databases, they handle unstructured data such as word-processing files, e-mail, multimedia, and social media efficiently. This paper discuss issues such as limitations, advantages, concerns and doubts regarding NoSQL databases.
Wide-band microstrip antenna with an H-shaped coupling aperture
(2002)

Theoretical and experimental results of a wide-band planar antenna are presented. This antenna can achieve a wide bandwidth, low cross-polarization levels, and low backward radiation levels. For wide bandwidth and easy integration with active circuits, it uses aperture-coupled stacked square patches. The coupling aperture is an H-shaped aperture. Based on the finite-difference time-domain method, a parametric study of the input impedance of the antenna is presented, and effects of each parameter on the antenna impedance are illustrated. One antenna is also designed, fabricated, and measured. The measured return loss exhibits an impedance bandwidth of 21.7%. The cross-polarization levels in both and planes are better than 23 dB. The front-to-back ratio of the antenna radiation pattern is better than 22 dB. Both theoretical and experimental results of parameters and radiation patterns are presented and discussed.
Putting Egocentric and Allocentric into Perspective
(2010)

In the last decade many studies examined egocentric and allocentric spatial relations. For various tasks, navigators profit from both kinds of relations. However, their interrelation seems to be underspecified. We present four elementary representations of allocentric and egocentric relations (sensorimotor contingencies, egocentric coordinate systems, allocentric coordinate systems, and perspective-free representations) and discuss them with respect to their encoding and retrieval. Elementary representations are problematic for capturing large spaces and situations which encompass both allocentric and egocentric relations at the same time. Complex spatial representations provide a solution to this problem. They combine elementary coordinate representations either by pair-wise connections or by hierarchical embedding. We discuss complex spatial representations with respect to computational requirements and their plausibility regarding behavioral and neural findings. This work is meant to clarify concepts of egocentric and allocentric, to show their limitations, benefits and empirical plausibility and to point out new directions for future research.
Novel Video Prediction for Large-scale Scene using Optical Flow
(2018)

Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction with an application to complex urban scenes. In contrast with previous work, the prediction model only requires video sequences and optical flow sequences for training and testing. Our method uses the rich spatial-temporal features in video sequences. The method takes advantage of the motion information extracting from optical flow maps between neighbor images as well as previous images. Empirical evaluations on the KITTI dataset and the Cityscapes dataset demonstrate the effectiveness of our method.
Impact of timing and frequency offsets on multicarrier waveform candidates for 5G
(2015)

This paper presents a study of the candidate waveforms for 5G when they are subject to timing and carrier frequency offset. These waveforms are: orthogonal frequency division multiplexing (OFDM), generalized frequency division multiplexing (GFDM), universal filtered multicarrier (UFMC), circular filter bank multicarrier (C-FBMC), and linear filter bank multicarrier (FBMC). We are particularly interested in multiple access interference (MAI) when a number of users transmit their signals to a base station in an asynchronous or a quasi-synchronous manner. We identify the source of MAI in these waveforms and present some numerical analysis that confirm our findings. The goal of this study is to answer the following question, “Which one of the 5G candidate waveforms has more relaxed synchronization requirements?”.
Contextmapping : experiences from practice
(2005)

In recent years, various methods and techniques have emerged for mapping the contexts of people's interaction with products. Designers and researchers use these techniques to gain deeper insight into the needs and dreams of prospective users of new products. As most of these techniques are still under development, there is a lack of practical knowledge about how such studies can be conducted. In this paper we share our insights, based on several projects from research and many years of industrial practice, of conducting user studies with generative techniques. The appendix contains a single case illustrating the application of these techniques in detail.
Enhancing Test Compression With Dependency Analysis for Multiple Expansion Ratios
(2017)

Scan test data compression is widely used in industry to reduce test data volume (TDV) and test application time (TAT). This paper shows how multiple scan chain expansion ratios can help to obtain high test data compression in system-on-chips. Scan chains are partitioned with a higher expansion ratio than normal in scan compression mode and then are gradually concatenated based on a cost function to detect any faults that could not be detected at the higher expansion ratios. It improves the overall test compression ratio since it potentially allows faults to be detected at the highest expansion ratio. This paper introduces a new cost function to choose scan chain concatenation candidates for concatenation for multiple expansion ratios. To avoid TDV and TAT increase by scan concatenation, the proposed method takes a logic structure and scan chain length into consideration. Experiment results show the proposed method reduces TAT and TDV by 53%–64% compared with a traditional scan compression method.
Implementation Challenges for Information Security Awareness Initiatives in E-Government
(2012)

With the widespread adoption of electronic government services, there has been a need to ensure a seamless flow of information across public sector organizations, while at the same time, maintaining confidentiality, integrity and availability. Governments have put in place various initiatives and programs including information security awareness to provide the needed understanding on how public sector employees can maintain security and privacy. Nonetheless, the implementation of such initiatives often faces a number of challenges that impede further take-up of e-government services. This paper aims to provide a better understanding of the challenges contributing towards the success of information security awareness initiatives implementation in the context of e-government. Political, organizational, social as well as technological challenges have been utilized in a conceptual framework to signify such challenges in e-government projects. An empirical case study conducted in a public sector organization in Greece was exploited in this research to reflect on these challenges. While, the results from this empirical study confirm the role of the identified challenges for the implementation of security awareness programs in e-government, it has been noticed that awareness programmers often pursue different targets of preserving security and privacy, which sometimes results in adding more complexity to the organization.
Positive emotions in early life and longevity: findings from the nun study.
(2001)

Handwritten autobiographies from 180 Catholic nuns, composed when participants were a mean age of 22 years, were scored for emotional content and related to survival during ages 75 to 95. A strong inverse association was found between positive emotional content in these writings and risk of mortality in late life (p < .001). As the quartile ranking of positive emotion in early life increased, there was a stepwise decrease in risk of mortality resulting in a 2.5-fold difference between the lowest and highest quartiles. Positive emotional content in early-life autobiographies was strongly associated with longevity 6 decades later. Underlying mechanisms of balanced emotional states are discussed.
Targetless Calibration of a Lidar - Perspective Camera Pair
(2013)

A novel method is proposed for the calibration of a camera - 3D lidar pair without the use of any special calibration pattern or point correspondences. The proposed method has no specific assumption about the data source: plain depth information is expected from the lidar scan and a simple perspective camera is used for the 2D images. The calibration is solved as a 2D-3D registration problem using a minimum of one (for extrinsic) or two (for intrinsic-extrinsic) planar regions visible in both cameras. The registration is then traced back to the solution of a non-linear system of equations which directly provides the calibration parameters between the bases of the two sensors. The method has been tested on a large set of synthetic lidar-camera image pairs as well as on real data acquired in outdoor environment.